By W. Ketchantang, S. Derrode, S. Bourennane, L. Martin (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)

This e-book constitutes the refereed complaints of the seventh overseas convention on complicated thoughts for clever imaginative and prescient platforms, ACIVS 2005, held in Antwerp, Belgium in September 2005.

The ninety revised complete papers offered have been rigorously reviewed and chosen from round 2 hundred submissions. The papers are geared up in topical sections on biometrics, class and popularity, content material and function characterization, photo and video research, snapshot and video coding, photo and video segmentation, scientific photo processing functions, movement estimation and monitoring, noise aid and restauration, and real-time processing and hardware.

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Additional resources for Advanced Concepts for Intelligent Vision Systems: 7th International Conference, ACIVS 2005, Antwerp, Belgium, September 20-23, 2005. Proceedings

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The training face set F consists of 6,304 highly variable face images, all cropped to the size of 24 × 24 pixels. We further build up a non-face repository with an initial set of 4,548 non-faces and 2,000 additional non-face scenery images for use in the bootstrapping procedure. For validation purposes, we use a separate test face set Vf consisting of 488 faces and a test non-face set Vn consisting of 23,573 non-faces. Each sample image is preprocessed to zero mean and unity standard deviation to reduce the interference of global illumination.

Of the IEEE Int. Conf. on Automatic Face and Gesture Recognition. (2000) 6. : Real-time fingertip tracking and gesture recognition. IEEE trans. on Computer Graphics and Applications 22 (2002) 64–71 7. : Human-computer interaction using gesture recognition and 3d hand tracking. In: Proc. of the IEEE Int. Conf. on Image Processing. (1998) 8. : Detection and estimation of pointing gestures in dense disparity maps. In: Proc. of the IEEE Int. Conf. on Automatic Face and Gesture Recognition. (2000) 9.

Detection speed on a Pentium-IV PC. It is also worth noticing that our approach also offers the advantage of training efficiency. Normally less than 1 hour is required to train a complete detector cascade, including the parameter selection (for some advanced training [3], around 15 days are required for a complete detector cascade). 5 Conclusions In this paper, we have presented a (near) real-time face detector using a cascade of neural network ensembles, with the following two advantages. First, we used a parallel ensemble technique within one component for improved detection accuracy.

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